Popescu and Etzioni , EMNLP 2005
This is a summary of research paper as part of Social Media Analysis 10-802, Fall 2012.
Contents
Citation
Ana-Maria Popescu , Oren Etzioni, Extracting product features and opinions from reviews, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, p.339-346, October 06-08, 2005, Vancouver, British Columbia, Canada.
Online Version
Abstract from the paper
Consumers are often forced to wade through many on-line reviews in order to make an informed product choice. This paper introduces OPINE, an unsupervised information-extraction system which mines reviews in order to build a model of important product features, their evaluation
by reviewers, and their relative quality across products.
Compared to previous work, OPINE achieves 22% higher precision (with only 3% lower recall) on the feature extraction task. OPINE’s novel use of
relaxation labeling for finding the semantic orientation of words in context leads to strong performance on the tasks of finding opinion phrases and their polarity.
Summary
Overview
This paper proposes various methods for opinion mining and classification of product reviews as positive or negative for specific product features. The paper describes four main sub-problems to deal with -
- Identifying product features/attributes
- Mining opinions about product features
- Determining opinion polarity
- Ranking opinions based on their strength
In order to solve the above sub tasks, this paper introduces OPINE, an unsupervised review mining system, built on top of the KnowItAll web information extraction system.
Proposed Techniques
Feature Selection
Sentiment Classification
Evaluation
Discussion
Related Papers
- Pang, B., L. Lee, and S. Vaithyanathan. 2002. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, 79–86.
- Turney, P. D. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, 417–424.
Study Plan
Resources useful for understanding this paper
- Article: Opinion Mining
- KnowItAll [O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. Weld, and A. Yates. 2005. Unsupervised named-entity extraction from the web: An experimental study. Artificial Intelligence, 165(1):91–134.]
